Small-Area Precipitation Forecasting and Drought--Flood Early Warning with Reverse-Martingale Regularized Recurrent Networks

Abstract

Small-area precipitation forecasts support real-time decisions for reservoir operation, irrigation planning, drought monitoring, and flash-flood response. Operational value depends not only on point accuracy, but also on calibrated exceedance probabilities and warning rules that remain stable when local weather regimes depart from the training climatology. We evaluate a reverse-martingale regularized recurrent neural network (\RMRNN) for probabilistic precipitation forecasting and sequential early warning. A backward-coherence penalty is added to the recurrent hidden state; the resulting residual process drives a Shiryaev--Roberts (SR) detector, so the same latent trajectory that produces the forecast also supplies a continuously updated drought or flood-regime indicator. The framework is tested on the Taiwan CWA dense rain-gauge network, CHIRPS v2 daily gridded precipitation over Taiwan and the Horn of Africa, and NOAA GHCN-Daily stations over the Texas Hill Country. Across 1{,}000 replications, \RMRNN{} matches or slightly improves the GRU baseline in RMSE, MAE, and CRPS at 1~h--72~h lead while substantially improving alarm characteristics. The SR detector reduces false-alarm ratios by a factor of three to five at matched detection power. In the 2020--2021 Taiwan drought, onset is flagged 8--12 days earlier than SPI-3 thresholding; in the 2023 Typhoon Haikui flood, flash-flood risk is signalled 4~h before the CWA operational alert.